Irregularity index: A new border irregularity measure for cutaneous melanocytic lesions

Lee, Tim K.; McLean, David I.; Atkins, M. Stella

doi:10.1016/s1361-8415(02)00090-7

Cited by 101 publications

(81 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To evaluate the discrimination, the contribution to the correct classification for each feature is evaluated [7]. Given a sample set of skin lesions with manually labeled classes w 1 and w 2 , representing benign moles and malignant melanomas respectively, the probability distributions ( ) .…”

Section: Performance Based Feature Selectionmentioning

confidence: 99%

“…In the previous decade, there have been many studies concerning the boundary irregularity of lesions using both geometric [7,8,9,10] and local fractal [11] measures. There are two types of irregularities found on boundaries: textual and structural, these correspond to fine changes and obvious convex and concave features respectively [7].…”

Section: Introductionmentioning

confidence: 99%

“…There are two types of irregularities found on boundaries: textual and structural, these correspond to fine changes and obvious convex and concave features respectively [7]. The structural irregularity has clinical importance for melanoma diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Previously T. K. Lee proposed an irregularity measure based on a deduced scale level using a curvature model of multi-scaled boundaries [7]. However it involved heavy computation as the proposed irregularity index was evaluated from a hierarchical structure with its roots as global curve segments and leaves as local segments for each of the consecutive smoothed contours.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Staunton

2013

Pattern Recognition

View full text Add to dashboard Cite

The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination.Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours.The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum.

show abstract

Section: Performance Based Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Staunton

2013

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Thus, the length of the array should be one less than the maximum possible depth of the tree which is defined in the loop termination conditions. Other features such as border irregularity index [10] can be extracted and used instead of CI; however, their time cost is higher than calculating CI.…”

Section: Feature Extractionmentioning

confidence: 99%

Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

KhakAbi

Lee

Atkins

2011

Machine Learning in Medical Imaging

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.

show abstract

Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures

Wahba¹,

Ashour²

2020

Engineering and Technology for Healthcare

View full text Add to dashboard Cite

Irregularity index: A new border irregularity measure for cutaneous melanocytic lesions

Cited by 101 publications

References 31 publications

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

Automated Diagnosis of Skin Cancer for Healthcare: Highlights and Procedures

Contact Info

Product

Resources

About